Overview

Dataset statistics

Number of variables28
Number of observations5267
Missing cells15758
Missing cells (%)10.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 MiB
Average record size in memory1.0 KiB

Variable types

Categorical12
Numeric10
Boolean5
DateTime1

Alerts

BM Unit ID has a high cardinality: 5267 distinct valuesHigh cardinality
BMU Name has a high cardinality: 5143 distinct valuesHigh cardinality
Party Name has a high cardinality: 349 distinct valuesHigh cardinality
Party ID has a high cardinality: 349 distinct valuesHigh cardinality
NGC BMU Name has a high cardinality: 1817 distinct valuesHigh cardinality
Trading Unit Name has a high cardinality: 52 distinct valuesHigh cardinality
TLF is highly overall correlated with GSP Group Id and 3 other fieldsHigh correlation
GC is highly overall correlated with WDBMCAEC and 4 other fieldsHigh correlation
DC is highly overall correlated with WDBMCAIC and 2 other fieldsHigh correlation
WDCALF is highly overall correlated with NWDCALF and 3 other fieldsHigh correlation
NWDCALF is highly overall correlated with WDCALF and 4 other fieldsHigh correlation
SECALF is highly overall correlated with WDCALF and 4 other fieldsHigh correlation
WDBMCAIC is highly overall correlated with DC and 3 other fieldsHigh correlation
NWDBMCAIC is highly overall correlated with DC and 3 other fieldsHigh correlation
WDBMCAEC is highly overall correlated with GC and 3 other fieldsHigh correlation
NWDBMCAEC is highly overall correlated with GC and 3 other fieldsHigh correlation
BMU Type is highly overall correlated with Trading Unit Name and 6 other fieldsHigh correlation
GSP Group Id is highly overall correlated with TLF and 3 other fieldsHigh correlation
GSP Group Name is highly overall correlated with TLF and 3 other fieldsHigh correlation
Trading Unit Name is highly overall correlated with TLF and 7 other fieldsHigh correlation
Prod/Cons Flag is highly overall correlated with GC and 6 other fieldsHigh correlation
Prod/Cons Status is highly overall correlated with GC and 7 other fieldsHigh correlation
Exempt Export Flag is highly overall correlated with BMU Type and 1 other fieldsHigh correlation
Base TU Flag is highly overall correlated with GC and 6 other fieldsHigh correlation
FPN Flag is highly overall correlated with SECALF and 4 other fieldsHigh correlation
Interconnector Id is highly overall correlated with TLF and 12 other fieldsHigh correlation
Manual Credit Qualifying Flag is highly overall correlated with GSP Group Id and 2 other fieldsHigh correlation
Credit Qualifying Status is highly overall correlated with BMU Type and 3 other fieldsHigh correlation
Exempt Export Flag is highly imbalanced (81.0%)Imbalance
Manual Credit Qualifying Flag is highly imbalanced (99.7%)Imbalance
Credit Qualifying Status is highly imbalanced (61.4%)Imbalance
NGC BMU Name has 3450 (65.5%) missing valuesMissing
GSP Group Id has 1115 (21.2%) missing valuesMissing
GSP Group Name has 1115 (21.2%) missing valuesMissing
Trading Unit Name has 1046 (19.9%) missing valuesMissing
Prod/Cons Flag has 4402 (83.6%) missing valuesMissing
Interconnector Id has 4591 (87.2%) missing valuesMissing
WDCALF is highly skewed (γ1 = -43.22665492)Skewed
NWDCALF is highly skewed (γ1 = -40.62443434)Skewed
WDBMCAEC is highly skewed (γ1 = -46.60787409)Skewed
NWDBMCAEC is highly skewed (γ1 = -42.31463183)Skewed
BM Unit ID is uniformly distributedUniform
BMU Name is uniformly distributedUniform
NGC BMU Name is uniformly distributedUniform
BM Unit ID has unique valuesUnique
GC has 3759 (71.4%) zerosZeros
DC has 3152 (59.8%) zerosZeros
WDCALF has 760 (14.4%) zerosZeros
NWDCALF has 764 (14.5%) zerosZeros
SECALF has 1313 (24.9%) zerosZeros
WDBMCAIC has 3500 (66.5%) zerosZeros
NWDBMCAIC has 3500 (66.5%) zerosZeros
WDBMCAEC has 4101 (77.9%) zerosZeros
NWDBMCAEC has 4101 (77.9%) zerosZeros

Reproduction

Analysis started2023-04-15 11:59:02.787106
Analysis finished2023-04-15 11:59:32.799885
Duration30.01 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

BM Unit ID
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct5267
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.6 KiB
2__AALAB000
 
1
C__APURE002
 
1
C__APGEN002
 
1
C__AOXPO009
 
1
C__AOXPO002
 
1
Other values (5262)
5262 

Length

Max length11
Median length11
Mean length10.752611
Min length6

Characters and Unicode

Total characters56634
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5267 ?
Unique (%)100.0%

Sample

1st row2__AALAB000
2nd row2__AALAS000
3rd row2__AALTI000
4th row2__AANGE000
5th row2__AANGE001

Common Values

ValueCountFrequency (%)
2__AALAB000 1
 
< 0.1%
C__APURE002 1
 
< 0.1%
C__APGEN002 1
 
< 0.1%
C__AOXPO009 1
 
< 0.1%
C__AOXPO002 1
 
< 0.1%
C__ANATP009 1
 
< 0.1%
C__ANATP002 1
 
< 0.1%
C__AMRCY009 1
 
< 0.1%
C__AMRCY002 1
 
< 0.1%
C__AMAGN009 1
 
< 0.1%
Other values (5257) 5257
99.8%

Length

2023-04-15T11:59:32.935236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2__aalab000 1
 
< 0.1%
2__abnrg000 1
 
< 0.1%
2__aange000 1
 
< 0.1%
2__aange001 1
 
< 0.1%
2__aange002 1
 
< 0.1%
2__aange003 1
 
< 0.1%
2__aange004 1
 
< 0.1%
2__aariz000 1
 
< 0.1%
2__aarru000 1
 
< 0.1%
2__aavro000 1
 
< 0.1%
Other values (5257) 5257
99.8%

Most occurring characters

ValueCountFrequency (%)
0 10892
19.2%
_ 9281
16.4%
2 3897
 
6.9%
E 2861
 
5.1%
I 2072
 
3.7%
N 1803
 
3.2%
C 1787
 
3.2%
G 1783
 
3.1%
D 1764
 
3.1%
A 1751
 
3.1%
Other values (28) 18743
33.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 29318
51.8%
Decimal Number 16828
29.7%
Connector Punctuation 9281
 
16.4%
Dash Punctuation 1207
 
2.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2861
 
9.8%
I 2072
 
7.1%
N 1803
 
6.1%
C 1787
 
6.1%
G 1783
 
6.1%
D 1764
 
6.0%
A 1751
 
6.0%
L 1693
 
5.8%
R 1657
 
5.7%
T 1518
 
5.2%
Other values (16) 10629
36.3%
Decimal Number
ValueCountFrequency (%)
0 10892
64.7%
2 3897
 
23.2%
1 1560
 
9.3%
3 168
 
1.0%
4 123
 
0.7%
5 69
 
0.4%
8 44
 
0.3%
9 42
 
0.2%
6 20
 
0.1%
7 13
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 9281
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1207
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29318
51.8%
Common 27316
48.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 2861
 
9.8%
I 2072
 
7.1%
N 1803
 
6.1%
C 1787
 
6.1%
G 1783
 
6.1%
D 1764
 
6.0%
A 1751
 
6.0%
L 1693
 
5.8%
R 1657
 
5.7%
T 1518
 
5.2%
Other values (16) 10629
36.3%
Common
ValueCountFrequency (%)
0 10892
39.9%
_ 9281
34.0%
2 3897
 
14.3%
1 1560
 
5.7%
- 1207
 
4.4%
3 168
 
0.6%
4 123
 
0.5%
5 69
 
0.3%
8 44
 
0.2%
9 42
 
0.2%
Other values (2) 33
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56634
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10892
19.2%
_ 9281
16.4%
2 3897
 
6.9%
E 2861
 
5.1%
I 2072
 
3.7%
N 1803
 
3.2%
C 1787
 
3.2%
G 1783
 
3.1%
D 1764
 
3.1%
A 1751
 
3.1%
Other values (28) 18743
33.1%

BMU Name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct5143
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size360.1 KiB
Flow into UK
 
16
Flow from UK
 
16
MA Energy Ltd
 
14
Cobblestone Energy DMCC
 
12
Cobblestone Energy Ltd
 
10
Other values (5138)
5199 

Length

Max length30
Median length11
Mean length12.98671
Min length3

Characters and Unicode

Total characters68401
Distinct characters70
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5095 ?
Unique (%)96.7%

Sample

1st rowALAB_A
2nd rowALAS_A
3rd row2__AALTI000
4th row2__AANGE000
5th row2__AANGE001

Common Values

ValueCountFrequency (%)
Flow into UK 16
 
0.3%
Flow from UK 16
 
0.3%
MA Energy Ltd 14
 
0.3%
Cobblestone Energy DMCC 12
 
0.2%
Cobblestone Energy Ltd 10
 
0.2%
Flow INTO UK 6
 
0.1%
GRM 4
 
0.1%
Flow FROM UK 4
 
0.1%
Brookfield E 4
 
0.1%
Energidk_G 3
 
0.1%
Other values (5133) 5178
98.3%

Length

2023-04-15T11:59:33.132268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
uk 86
 
1.1%
flow 76
 
1.0%
1 69
 
0.9%
2 58
 
0.8%
55
 
0.7%
energy 47
 
0.6%
wind 47
 
0.6%
farm 42
 
0.6%
unit 42
 
0.6%
from 38
 
0.5%
Other values (4983) 6943
92.5%

Most occurring characters

ValueCountFrequency (%)
0 8762
 
12.8%
_ 7414
 
10.8%
A 3402
 
5.0%
E 3296
 
4.8%
2 3039
 
4.4%
2248
 
3.3%
N 2093
 
3.1%
C 2047
 
3.0%
- 1949
 
2.8%
G 1707
 
2.5%
Other values (60) 32444
47.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 32138
47.0%
Decimal Number 14532
21.2%
Lowercase Letter 10089
 
14.7%
Connector Punctuation 7414
 
10.8%
Space Separator 2248
 
3.3%
Dash Punctuation 1949
 
2.8%
Other Punctuation 13
 
< 0.1%
Open Punctuation 9
 
< 0.1%
Close Punctuation 9
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3402
 
10.6%
E 3296
 
10.3%
N 2093
 
6.5%
C 2047
 
6.4%
G 1707
 
5.3%
L 1707
 
5.3%
R 1613
 
5.0%
O 1544
 
4.8%
D 1507
 
4.7%
S 1461
 
4.5%
Other values (16) 11761
36.6%
Lowercase Letter
ValueCountFrequency (%)
e 1239
12.3%
o 1003
9.9%
r 999
9.9%
n 903
 
9.0%
t 815
 
8.1%
a 757
 
7.5%
i 642
 
6.4%
l 550
 
5.5%
d 453
 
4.5%
s 383
 
3.8%
Other values (15) 2345
23.2%
Decimal Number
ValueCountFrequency (%)
0 8762
60.3%
2 3039
 
20.9%
1 1476
 
10.2%
7 245
 
1.7%
8 232
 
1.6%
9 202
 
1.4%
3 199
 
1.4%
4 164
 
1.1%
6 142
 
1.0%
5 71
 
0.5%
Other Punctuation
ValueCountFrequency (%)
/ 6
46.2%
. 3
23.1%
& 3
23.1%
' 1
 
7.7%
Connector Punctuation
ValueCountFrequency (%)
_ 7414
100.0%
Space Separator
ValueCountFrequency (%)
2248
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1949
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42227
61.7%
Common 26174
38.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3402
 
8.1%
E 3296
 
7.8%
N 2093
 
5.0%
C 2047
 
4.8%
G 1707
 
4.0%
L 1707
 
4.0%
R 1613
 
3.8%
O 1544
 
3.7%
D 1507
 
3.6%
S 1461
 
3.5%
Other values (41) 21850
51.7%
Common
ValueCountFrequency (%)
0 8762
33.5%
_ 7414
28.3%
2 3039
 
11.6%
2248
 
8.6%
- 1949
 
7.4%
1 1476
 
5.6%
7 245
 
0.9%
8 232
 
0.9%
9 202
 
0.8%
3 199
 
0.8%
Other values (9) 408
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68401
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8762
 
12.8%
_ 7414
 
10.8%
A 3402
 
5.0%
E 3296
 
4.8%
2 3039
 
4.4%
2248
 
3.3%
N 2093
 
3.1%
C 2047
 
3.0%
- 1949
 
2.8%
G 1707
 
2.5%
Other values (60) 32444
47.4%

Party Name
Categorical

Distinct349
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size408.9 KiB
British Gas Trading Ltd
 
318
EDF Energy Customers Ltd
 
218
SP Energy Retail Ltd
 
143
OVO Electricity Ltd
 
127
Statkraft Markets Gmbh
 
93
Other values (344)
4368 

Length

Max length30
Median length26
Mean length22.471806
Min length3

Characters and Unicode

Total characters118359
Distinct characters65
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)1.1%

Sample

1st rowSquare1 Energy Limited
2nd rowClean Energy Supply Ltd
3rd rowCo-operative Energy Limited
4th rowLimejump Energy Limited
5th rowLimejump Energy Limited

Common Values

ValueCountFrequency (%)
British Gas Trading Ltd 318
 
6.0%
EDF Energy Customers Ltd 218
 
4.1%
SP Energy Retail Ltd 143
 
2.7%
OVO Electricity Ltd 127
 
2.4%
Statkraft Markets Gmbh 93
 
1.8%
Limejump Energy Limited 91
 
1.7%
Octopus Energy Limited 90
 
1.7%
Drax Energy Solutions Limited 85
 
1.6%
Shell Energy Retail Limited 84
 
1.6%
Utilita Energy Limited 75
 
1.4%
Other values (339) 3943
74.9%

Length

2023-04-15T11:59:33.395154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
energy 2940
 
15.7%
limited 2404
 
12.9%
ltd 1984
 
10.6%
trading 668
 
3.6%
gas 569
 
3.0%
power 452
 
2.4%
supply 414
 
2.2%
uk 406
 
2.2%
retail 325
 
1.7%
british 318
 
1.7%
Other values (455) 8203
43.9%

Most occurring characters

ValueCountFrequency (%)
13430
 
11.3%
e 10075
 
8.5%
i 8367
 
7.1%
t 7550
 
6.4%
r 7026
 
5.9%
n 5691
 
4.8%
d 5371
 
4.5%
E 5311
 
4.5%
L 4694
 
4.0%
a 3964
 
3.3%
Other values (55) 46880
39.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 75999
64.2%
Uppercase Letter 27736
 
23.4%
Space Separator 13430
 
11.3%
Other Punctuation 581
 
0.5%
Open Punctuation 217
 
0.2%
Close Punctuation 209
 
0.2%
Decimal Number 115
 
0.1%
Dash Punctuation 72
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10075
13.3%
i 8367
11.0%
t 7550
9.9%
r 7026
9.2%
n 5691
 
7.5%
d 5371
 
7.1%
a 3964
 
5.2%
g 3921
 
5.2%
y 3799
 
5.0%
m 3367
 
4.4%
Other values (16) 16868
22.2%
Uppercase Letter
ValueCountFrequency (%)
E 5311
19.1%
L 4694
16.9%
S 2378
 
8.6%
T 1547
 
5.6%
G 1506
 
5.4%
R 1159
 
4.2%
C 1151
 
4.1%
P 1101
 
4.0%
O 1043
 
3.8%
D 1018
 
3.7%
Other values (16) 6828
24.6%
Decimal Number
ValueCountFrequency (%)
2 39
33.9%
4 28
24.3%
1 19
16.5%
3 15
 
13.0%
9 14
 
12.2%
Other Punctuation
ValueCountFrequency (%)
. 240
41.3%
& 216
37.2%
/ 111
19.1%
* 14
 
2.4%
Space Separator
ValueCountFrequency (%)
13430
100.0%
Open Punctuation
ValueCountFrequency (%)
( 217
100.0%
Close Punctuation
ValueCountFrequency (%)
) 209
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 103735
87.6%
Common 14624
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10075
 
9.7%
i 8367
 
8.1%
t 7550
 
7.3%
r 7026
 
6.8%
n 5691
 
5.5%
d 5371
 
5.2%
E 5311
 
5.1%
L 4694
 
4.5%
a 3964
 
3.8%
g 3921
 
3.8%
Other values (42) 41765
40.3%
Common
ValueCountFrequency (%)
13430
91.8%
. 240
 
1.6%
( 217
 
1.5%
& 216
 
1.5%
) 209
 
1.4%
/ 111
 
0.8%
- 72
 
0.5%
2 39
 
0.3%
4 28
 
0.2%
1 19
 
0.1%
Other values (3) 43
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13430
 
11.3%
e 10075
 
8.5%
i 8367
 
7.1%
t 7550
 
6.4%
r 7026
 
5.9%
n 5691
 
4.8%
d 5371
 
4.5%
E 5311
 
4.5%
L 4694
 
4.0%
a 3964
 
3.3%
Other values (55) 46880
39.6%

Party ID
Categorical

Distinct349
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size325.0 KiB
BRITGAS
 
318
LENCO
 
218
SPSUP01
 
143
OVOE
 
127
STATKRA1
 
93
Other values (344)
4368 

Length

Max length8
Median length7
Mean length6.1591039
Min length2

Characters and Unicode

Total characters32440
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)1.1%

Sample

1st rowALAB
2nd rowALAS
3rd rowVOLA
4th rowANGEL
5th rowANGEL

Common Values

ValueCountFrequency (%)
BRITGAS 318
 
6.0%
LENCO 218
 
4.1%
SPSUP01 143
 
2.7%
OVOE 127
 
2.4%
STATKRA1 93
 
1.8%
ANGEL 91
 
1.7%
MERCURY 90
 
1.7%
HAVEN 85
 
1.6%
FRST01 84
 
1.6%
UTILITA 75
 
1.4%
Other values (339) 3943
74.9%

Length

2023-04-15T11:59:33.687876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
britgas 318
 
6.0%
lenco 218
 
4.1%
spsup01 143
 
2.7%
ovoe 127
 
2.4%
statkra1 93
 
1.8%
angel 91
 
1.7%
mercury 90
 
1.7%
haven 85
 
1.6%
frst01 84
 
1.6%
utilita 75
 
1.4%
Other values (339) 3943
74.9%

Most occurring characters

ValueCountFrequency (%)
E 3800
 
11.7%
R 2556
 
7.9%
A 2406
 
7.4%
N 2277
 
7.0%
S 1999
 
6.2%
L 1880
 
5.8%
O 1876
 
5.8%
G 1822
 
5.6%
T 1812
 
5.6%
I 1409
 
4.3%
Other values (26) 10603
32.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 30827
95.0%
Decimal Number 1613
 
5.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 3800
12.3%
R 2556
 
8.3%
A 2406
 
7.8%
N 2277
 
7.4%
S 1999
 
6.5%
L 1880
 
6.1%
O 1876
 
6.1%
G 1822
 
5.9%
T 1812
 
5.9%
I 1409
 
4.6%
Other values (16) 8990
29.2%
Decimal Number
ValueCountFrequency (%)
0 690
42.8%
1 637
39.5%
2 121
 
7.5%
3 42
 
2.6%
5 40
 
2.5%
4 37
 
2.3%
7 17
 
1.1%
6 15
 
0.9%
9 12
 
0.7%
8 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 30827
95.0%
Common 1613
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 3800
12.3%
R 2556
 
8.3%
A 2406
 
7.8%
N 2277
 
7.4%
S 1999
 
6.5%
L 1880
 
6.1%
O 1876
 
6.1%
G 1822
 
5.9%
T 1812
 
5.9%
I 1409
 
4.6%
Other values (16) 8990
29.2%
Common
ValueCountFrequency (%)
0 690
42.8%
1 637
39.5%
2 121
 
7.5%
3 42
 
2.6%
5 40
 
2.5%
4 37
 
2.3%
7 17
 
1.1%
6 15
 
0.9%
9 12
 
0.7%
8 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 3800
 
11.7%
R 2556
 
7.9%
A 2406
 
7.4%
N 2277
 
7.0%
S 1999
 
6.2%
L 1880
 
5.8%
O 1876
 
5.8%
G 1822
 
5.6%
T 1812
 
5.6%
I 1409
 
4.3%
Other values (26) 10603
32.7%

BMU Type
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size298.5 KiB
G
3038 
S
917 
I
676 
T
439 
E
 
158

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5267
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG
2nd rowG
3rd rowG
4th rowG
5th rowS

Common Values

ValueCountFrequency (%)
G 3038
57.7%
S 917
 
17.4%
I 676
 
12.8%
T 439
 
8.3%
E 158
 
3.0%
V 39
 
0.7%

Length

2023-04-15T11:59:33.962487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-15T11:59:34.244169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
g 3038
57.7%
s 917
 
17.4%
i 676
 
12.8%
t 439
 
8.3%
e 158
 
3.0%
v 39
 
0.7%

Most occurring characters

ValueCountFrequency (%)
G 3038
57.7%
S 917
 
17.4%
I 676
 
12.8%
T 439
 
8.3%
E 158
 
3.0%
V 39
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5267
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 3038
57.7%
S 917
 
17.4%
I 676
 
12.8%
T 439
 
8.3%
E 158
 
3.0%
V 39
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 5267
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 3038
57.7%
S 917
 
17.4%
I 676
 
12.8%
T 439
 
8.3%
E 158
 
3.0%
V 39
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 3038
57.7%
S 917
 
17.4%
I 676
 
12.8%
T 439
 
8.3%
E 158
 
3.0%
V 39
 
0.7%

NGC BMU Name
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct1817
Distinct (%)100.0%
Missing3450
Missing (%)65.5%
Memory size224.0 KiB
IIG-LTFD1
 
1
ILD-HELI1
 
1
ILD-HART1
 
1
ILD-GLEN1
 
1
ILD-EPCO1
 
1
Other values (1812)
1812 

Length

Max length9
Median length9
Mean length8.3808476
Min length5

Characters and Unicode

Total characters15228
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1817 ?
Unique (%)100.0%

Sample

1st rowAG-ALIM03
2nd rowEAS-AXPO1
3rd rowEAS-BGS01
4th rowEAS-BIZ01
5th rowEAS-BRT01

Common Values

ValueCountFrequency (%)
IIG-LTFD1 1
 
< 0.1%
ILD-HELI1 1
 
< 0.1%
ILD-HART1 1
 
< 0.1%
ILD-GLEN1 1
 
< 0.1%
ILD-EPCO1 1
 
< 0.1%
ILD-ENSC1 1
 
< 0.1%
ILD-ELTR1 1
 
< 0.1%
ILD-EDMK1 1
 
< 0.1%
ILD-EDFT1 1
 
< 0.1%
ILD-DVAL1 1
 
< 0.1%
Other values (1807) 1807
34.3%
(Missing) 3450
65.5%

Length

2023-04-15T11:59:34.449298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
iig-ltfd1 1
 
0.1%
ag-rec06b 1
 
0.1%
eas-bgs01 1
 
0.1%
eas-biz01 1
 
0.1%
eas-brt01 1
 
0.1%
petdg-1 1
 
0.1%
ag-aeco01 1
 
0.1%
eas-edf01 1
 
0.1%
eas-edr01 1
 
0.1%
eas-eas01 1
 
0.1%
Other values (1807) 1807
99.4%

Most occurring characters

ValueCountFrequency (%)
- 1772
 
11.6%
1 1529
 
10.0%
E 1002
 
6.6%
I 908
 
6.0%
G 837
 
5.5%
S 808
 
5.3%
D 749
 
4.9%
N 718
 
4.7%
0 675
 
4.4%
A 655
 
4.3%
Other values (27) 5575
36.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10842
71.2%
Decimal Number 2614
 
17.2%
Dash Punctuation 1772
 
11.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1002
 
9.2%
I 908
 
8.4%
G 837
 
7.7%
S 808
 
7.5%
D 749
 
6.9%
N 718
 
6.6%
A 655
 
6.0%
L 582
 
5.4%
T 494
 
4.6%
W 464
 
4.3%
Other values (16) 3625
33.4%
Decimal Number
ValueCountFrequency (%)
1 1529
58.5%
0 675
25.8%
2 256
 
9.8%
3 60
 
2.3%
4 41
 
1.6%
5 18
 
0.7%
6 17
 
0.7%
7 10
 
0.4%
8 6
 
0.2%
9 2
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1772
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10842
71.2%
Common 4386
28.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1002
 
9.2%
I 908
 
8.4%
G 837
 
7.7%
S 808
 
7.5%
D 749
 
6.9%
N 718
 
6.6%
A 655
 
6.0%
L 582
 
5.4%
T 494
 
4.6%
W 464
 
4.3%
Other values (16) 3625
33.4%
Common
ValueCountFrequency (%)
- 1772
40.4%
1 1529
34.9%
0 675
 
15.4%
2 256
 
5.8%
3 60
 
1.4%
4 41
 
0.9%
5 18
 
0.4%
6 17
 
0.4%
7 10
 
0.2%
8 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1772
 
11.6%
1 1529
 
10.0%
E 1002
 
6.6%
I 908
 
6.0%
G 837
 
5.5%
S 808
 
5.3%
D 749
 
4.9%
N 718
 
4.7%
0 675
 
4.4%
A 655
 
4.3%
Other values (27) 5575
36.6%

GSP Group Id
Categorical

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)0.3%
Missing1115
Missing (%)21.2%
Memory size274.2 KiB
_D
378 
_A
352 
_P
352 
_F
332 
_N
309 
Other values (9)
2429 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters8304
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row_A
2nd row_A
3rd row_A
4th row_A
5th row_A

Common Values

ValueCountFrequency (%)
_D 378
 
7.2%
_A 352
 
6.7%
_P 352
 
6.7%
_F 332
 
6.3%
_N 309
 
5.9%
_M 300
 
5.7%
_K 289
 
5.5%
_J 281
 
5.3%
_G 273
 
5.2%
_H 272
 
5.2%
Other values (4) 1014
19.3%
(Missing) 1115
21.2%

Length

2023-04-15T11:59:34.643866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d 378
 
9.1%
a 352
 
8.5%
p 352
 
8.5%
f 332
 
8.0%
n 309
 
7.4%
m 300
 
7.2%
k 289
 
7.0%
j 281
 
6.8%
g 273
 
6.6%
h 272
 
6.6%
Other values (4) 1014
24.4%

Most occurring characters

ValueCountFrequency (%)
_ 4152
50.0%
D 378
 
4.6%
A 352
 
4.2%
P 352
 
4.2%
F 332
 
4.0%
N 309
 
3.7%
M 300
 
3.6%
K 289
 
3.5%
J 281
 
3.4%
G 273
 
3.3%
Other values (5) 1286
 
15.5%

Most occurring categories

ValueCountFrequency (%)
Connector Punctuation 4152
50.0%
Uppercase Letter 4152
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 378
 
9.1%
A 352
 
8.5%
P 352
 
8.5%
F 332
 
8.0%
N 309
 
7.4%
M 300
 
7.2%
K 289
 
7.0%
J 281
 
6.8%
G 273
 
6.6%
H 272
 
6.6%
Other values (4) 1014
24.4%
Connector Punctuation
ValueCountFrequency (%)
_ 4152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4152
50.0%
Latin 4152
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 378
 
9.1%
A 352
 
8.5%
P 352
 
8.5%
F 332
 
8.0%
N 309
 
7.4%
M 300
 
7.2%
K 289
 
7.0%
J 281
 
6.8%
G 273
 
6.6%
H 272
 
6.6%
Other values (4) 1014
24.4%
Common
ValueCountFrequency (%)
_ 4152
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 4152
50.0%
D 378
 
4.6%
A 352
 
4.2%
P 352
 
4.2%
F 332
 
4.0%
N 309
 
3.7%
M 300
 
3.6%
K 289
 
3.5%
J 281
 
3.4%
G 273
 
3.3%
Other values (5) 1286
 
15.5%

GSP Group Name
Categorical

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)0.3%
Missing1115
Missing (%)21.2%
Memory size328.2 KiB
Merseyside and North Wales
378 
Eastern GSP Group
352 
NORTH of SCOTLAND GSP
352 
NORTHERN
332 
SOUTH of SCOTLAND GSP
309 
Other values (9)
2429 

Length

Max length26
Median length17
Mean length15.312139
Min length8

Characters and Unicode

Total characters63576
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEastern GSP Group
2nd rowEastern GSP Group
3rd rowEastern GSP Group
4th rowEastern GSP Group
5th rowEastern GSP Group

Common Values

ValueCountFrequency (%)
Merseyside and North Wales 378
 
7.2%
Eastern GSP Group 352
 
6.7%
NORTH of SCOTLAND GSP 352
 
6.7%
NORTHERN 332
 
6.3%
SOUTH of SCOTLAND GSP 309
 
5.9%
Yorkshire Electricity 300
 
5.7%
South Wales 289
 
5.5%
South Eastern 281
 
5.3%
North Western 273
 
5.2%
Southern 272
 
5.2%
Other values (4) 1014
19.3%
(Missing) 1115
21.2%

Length

2023-04-15T11:59:34.826417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
south 1134
11.5%
gsp 1013
 
10.3%
north 1003
 
10.2%
wales 667
 
6.8%
of 661
 
6.7%
scotland 661
 
6.7%
eastern 633
 
6.4%
western 528
 
5.3%
midlands 525
 
5.3%
merseyside 378
 
3.8%
Other values (9) 2671
27.1%

Most occurring characters

ValueCountFrequency (%)
5722
 
9.0%
e 4362
 
6.9%
t 4246
 
6.7%
r 3948
 
6.2%
s 3912
 
6.2%
o 3295
 
5.2%
S 3080
 
4.8%
n 2570
 
4.0%
i 2505
 
3.9%
a 2472
 
3.9%
Other values (26) 27464
43.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37164
58.5%
Uppercase Letter 20690
32.5%
Space Separator 5722
 
9.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4362
11.7%
t 4246
11.4%
r 3948
10.6%
s 3912
10.5%
o 3295
8.9%
n 2570
6.9%
i 2505
6.7%
a 2472
6.7%
h 2048
 
5.5%
d 1806
 
4.9%
Other values (8) 6000
16.1%
Uppercase Letter
ValueCountFrequency (%)
S 3080
14.9%
N 2328
11.3%
E 1768
 
8.5%
T 1654
 
8.0%
O 1654
 
8.0%
G 1365
 
6.6%
W 1195
 
5.8%
R 1016
 
4.9%
P 1013
 
4.9%
H 993
 
4.8%
Other values (7) 4624
22.3%
Space Separator
ValueCountFrequency (%)
5722
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57854
91.0%
Common 5722
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4362
 
7.5%
t 4246
 
7.3%
r 3948
 
6.8%
s 3912
 
6.8%
o 3295
 
5.7%
S 3080
 
5.3%
n 2570
 
4.4%
i 2505
 
4.3%
a 2472
 
4.3%
N 2328
 
4.0%
Other values (25) 25136
43.4%
Common
ValueCountFrequency (%)
5722
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5722
 
9.0%
e 4362
 
6.9%
t 4246
 
6.7%
r 3948
 
6.2%
s 3912
 
6.2%
o 3295
 
5.2%
S 3080
 
4.8%
n 2570
 
4.0%
i 2505
 
3.9%
a 2472
 
3.9%
Other values (26) 27464
43.2%

Trading Unit Name
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct52
Distinct (%)1.2%
Missing1046
Missing (%)19.9%
Memory size310.6 KiB
DEFAULT__D
374 
DEFAULT__P
348 
DEFAULT__A
342 
DEFAULT__F
327 
DEFAULT__N
306 
Other values (47)
2524 

Length

Max length30
Median length10
Mean length10.379057
Min length6

Characters and Unicode

Total characters43810
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEFAULT__A
2nd rowDEFAULT__A
3rd rowDEFAULT__A
4th rowDEFAULT__A
5th rowDEFAULT__A

Common Values

ValueCountFrequency (%)
DEFAULT__D 374
 
7.1%
DEFAULT__P 348
 
6.6%
DEFAULT__A 342
 
6.5%
DEFAULT__F 327
 
6.2%
DEFAULT__N 306
 
5.8%
DEFAULT__M 292
 
5.5%
DEFAULT__K 285
 
5.4%
DEFAULT__J 278
 
5.3%
DEFAULT__G 268
 
5.1%
DEFAULT__B 260
 
4.9%
Other values (42) 1141
21.7%
(Missing) 1046
19.9%

Length

2023-04-15T11:59:35.117686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
default__d 374
 
8.2%
default__p 348
 
7.6%
default__a 342
 
7.5%
default__f 327
 
7.2%
default__n 306
 
6.7%
default__m 292
 
6.4%
default__k 285
 
6.2%
default__j 278
 
6.1%
default__g 268
 
5.9%
default__b 260
 
5.7%
Other values (64) 1482
32.5%

Most occurring characters

ValueCountFrequency (%)
_ 8140
18.6%
D 4486
10.2%
A 4478
10.2%
F 4404
10.1%
E 4366
10.0%
L 4341
9.9%
T 4162
9.5%
U 4107
9.4%
P 477
 
1.1%
N 382
 
0.9%
Other values (46) 4467
10.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 33671
76.9%
Connector Punctuation 8140
 
18.6%
Lowercase Letter 1604
 
3.7%
Space Separator 360
 
0.8%
Other Punctuation 23
 
0.1%
Decimal Number 8
 
< 0.1%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 4486
13.3%
A 4478
13.3%
F 4404
13.1%
E 4366
13.0%
L 4341
12.9%
T 4162
12.4%
U 4107
12.2%
P 477
 
1.4%
N 382
 
1.1%
G 322
 
1.0%
Other values (15) 2146
6.4%
Lowercase Letter
ValueCountFrequency (%)
t 239
14.9%
o 216
13.5%
e 160
10.0%
n 151
9.4%
a 148
9.2%
r 146
9.1%
i 129
8.0%
w 92
 
5.7%
s 48
 
3.0%
y 46
 
2.9%
Other values (13) 229
14.3%
Other Punctuation
ValueCountFrequency (%)
& 12
52.2%
. 5
21.7%
' 4
 
17.4%
/ 2
 
8.7%
Connector Punctuation
ValueCountFrequency (%)
_ 8140
100.0%
Space Separator
ValueCountFrequency (%)
360
100.0%
Decimal Number
ValueCountFrequency (%)
1 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35275
80.5%
Common 8535
 
19.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 4486
12.7%
A 4478
12.7%
F 4404
12.5%
E 4366
12.4%
L 4341
12.3%
T 4162
11.8%
U 4107
11.6%
P 477
 
1.4%
N 382
 
1.1%
G 322
 
0.9%
Other values (38) 3750
10.6%
Common
ValueCountFrequency (%)
_ 8140
95.4%
360
 
4.2%
& 12
 
0.1%
1 8
 
0.1%
. 5
 
0.1%
' 4
 
< 0.1%
- 4
 
< 0.1%
/ 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 8140
18.6%
D 4486
10.2%
A 4478
10.2%
F 4404
10.1%
E 4366
10.0%
L 4341
9.9%
T 4162
9.5%
U 4107
9.4%
P 477
 
1.1%
N 382
 
0.9%
Other values (46) 4467
10.2%

Prod/Cons Flag
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing4402
Missing (%)83.6%
Memory size186.7 KiB
P
438 
C
427 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters865
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowP
4th rowP
5th rowP

Common Values

ValueCountFrequency (%)
P 438
 
8.3%
C 427
 
8.1%
(Missing) 4402
83.6%

Length

2023-04-15T11:59:35.344129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-15T11:59:35.529263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
p 438
50.6%
c 427
49.4%

Most occurring characters

ValueCountFrequency (%)
P 438
50.6%
C 427
49.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 865
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 438
50.6%
C 427
49.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 865
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 438
50.6%
C 427
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 865
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 438
50.6%
C 427
49.4%

Prod/Cons Status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size298.5 KiB
C
4536 
P
731 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5267
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 4536
86.1%
P 731
 
13.9%

Length

2023-04-15T11:59:35.683489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-15T11:59:35.861589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
c 4536
86.1%
p 731
 
13.9%

Most occurring characters

ValueCountFrequency (%)
C 4536
86.1%
P 731
 
13.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5267
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 4536
86.1%
P 731
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 5267
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 4536
86.1%
P 731
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 4536
86.1%
P 731
 
13.9%

TLF
Real number (ℝ)

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0022847549
Minimum-0.0261238
Maximum0.0148608
Zeros0
Zeros (%)0.0%
Negative1553
Negative (%)29.5%
Memory size41.3 KiB
2023-04-15T11:59:36.065733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.0261238
5-th percentile-0.0261238
Q1-0.0037297
median0.0064211
Q30.0073762
95-th percentile0.0145587
Maximum0.0148608
Range0.0409846
Interquartile range (IQR)0.0111059

Descriptive statistics

Standard deviation0.010762974
Coefficient of variation (CV)4.7107786
Kurtosis1.4374336
Mean0.0022847549
Median Absolute Deviation (MAD)0.0058645
Skewness-1.356837
Sum12.033804
Variance0.00011584162
MonotonicityNot monotonic
2023-04-15T11:59:36.286856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0064211 778
14.8%
0.0070704 465
8.8%
-0.0261238 442
8.4%
-0.0042622 426
 
8.1%
0.0122856 410
 
7.8%
0.002763 390
 
7.4%
-0.0037297 348
 
6.6%
-0.0083696 337
 
6.4%
0.0073762 305
 
5.8%
0.0061409 301
 
5.7%
Other values (4) 1065
20.2%
ValueCountFrequency (%)
-0.0261238 442
8.4%
-0.0083696 337
6.4%
-0.0042622 426
8.1%
-0.0037297 348
6.6%
0.0008159 293
 
5.6%
0.002763 390
7.4%
0.0061409 301
 
5.7%
0.0064211 778
14.8%
0.0070704 465
8.8%
0.0073762 305
 
5.8%
ValueCountFrequency (%)
0.0148608 262
 
5.0%
0.0145587 245
 
4.7%
0.0125248 265
 
5.0%
0.0122856 410
7.8%
0.0073762 305
 
5.8%
0.0070704 465
8.8%
0.0064211 778
14.8%
0.0061409 301
 
5.7%
0.002763 390
7.4%
0.0008159 293
 
5.6%

GC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct672
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.157057
Minimum0
Maximum4000
Zeros3759
Zeros (%)71.4%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2023-04-15T11:59:36.474806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile897.428
Maximum4000
Range4000
Interquartile range (IQR)6

Descriptive statistics

Standard deviation317.92134
Coefficient of variation (CV)3.4876218
Kurtosis36.165946
Mean91.157057
Median Absolute Deviation (MAD)0
Skewness5.2399865
Sum480124.22
Variance101073.98
MonotonicityNot monotonic
2023-04-15T11:59:36.694013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3759
71.4%
1000 169
 
3.2%
49 56
 
1.1%
2000 47
 
0.9%
500 45
 
0.9%
50 44
 
0.8%
20 41
 
0.8%
40 20
 
0.4%
25 20
 
0.4%
2 17
 
0.3%
Other values (662) 1049
 
19.9%
ValueCountFrequency (%)
0 3759
71.4%
0.02 6
 
0.1%
0.04 2
 
< 0.1%
0.06 1
 
< 0.1%
0.08 4
 
0.1%
0.1 1
 
< 0.1%
0.12 2
 
< 0.1%
0.14 1
 
< 0.1%
0.2 1
 
< 0.1%
0.22 2
 
< 0.1%
ValueCountFrequency (%)
4000 4
 
0.1%
3000 4
 
0.1%
2880 1
 
< 0.1%
2000 47
0.9%
1760 1
 
< 0.1%
1710.204 1
 
< 0.1%
1500 3
 
0.1%
1478.2 1
 
< 0.1%
1440 1
 
< 0.1%
1400 5
 
0.1%

DC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1020
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-80.589158
Minimum-4000
Maximum0
Zeros3152
Zeros (%)59.8%
Negative2115
Negative (%)40.2%
Memory size41.3 KiB
2023-04-15T11:59:36.985160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4000
5-th percentile-589.976
Q1-4.177
median0
Q30
95-th percentile0
Maximum0
Range4000
Interquartile range (IQR)4.177

Descriptive statistics

Standard deviation306.43307
Coefficient of variation (CV)-3.8024106
Kurtosis43.118175
Mean-80.589158
Median Absolute Deviation (MAD)0
Skewness-5.6985584
Sum-424463.1
Variance93901.227
MonotonicityNot monotonic
2023-04-15T11:59:37.294795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3152
59.8%
-1000 167
 
3.2%
-2 112
 
2.1%
-1 75
 
1.4%
-2000 47
 
0.9%
-500 42
 
0.8%
-0.1 33
 
0.6%
-10 28
 
0.5%
-6 26
 
0.5%
-0.2 25
 
0.5%
Other values (1010) 1560
29.6%
ValueCountFrequency (%)
-4000 5
 
0.1%
-3000 2
 
< 0.1%
-2000 47
0.9%
-1500 2
 
< 0.1%
-1480 3
 
0.1%
-1449.88 1
 
< 0.1%
-1400 2
 
< 0.1%
-1200 4
 
0.1%
-1150 2
 
< 0.1%
-1114.47 1
 
< 0.1%
ValueCountFrequency (%)
0 3152
59.8%
-0.002 1
 
< 0.1%
-0.004 5
 
0.1%
-0.006 3
 
0.1%
-0.012 1
 
< 0.1%
-0.016 1
 
< 0.1%
-0.018 2
 
< 0.1%
-0.02 24
 
0.5%
-0.028 1
 
< 0.1%
-0.036 1
 
< 0.1%

WDCALF
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1286
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25342438
Minimum-152.656
Maximum1
Zeros760
Zeros (%)14.4%
Negative66
Negative (%)1.3%
Memory size41.3 KiB
2023-04-15T11:59:37.524535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-152.656
5-th percentile0
Q10.3852
median0.4166
Q30.439
95-th percentile0.58944
Maximum1
Range153.656
Interquartile range (IQR)0.0538

Descriptive statistics

Standard deviation2.7936352
Coefficient of variation (CV)11.023546
Kurtosis2094.3599
Mean0.25342438
Median Absolute Deviation (MAD)0.0224
Skewness-43.226655
Sum1334.7862
Variance7.8043979
MonotonicityNot monotonic
2023-04-15T11:59:37.736416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 760
 
14.4%
0.4 398
 
7.6%
0.4471 265
 
5.0%
0.4313 238
 
4.5%
0.419 235
 
4.5%
0.3955 226
 
4.3%
0.4016 191
 
3.6%
0.4126 190
 
3.6%
0.4166 189
 
3.6%
0.4277 182
 
3.5%
Other values (1276) 2393
45.4%
ValueCountFrequency (%)
-152.656 1
< 0.1%
-100.2359 1
< 0.1%
-68.4484 1
< 0.1%
-26.6626 1
< 0.1%
-23.5 1
< 0.1%
-16.651 1
< 0.1%
-13.546 1
< 0.1%
-12.6936 1
< 0.1%
-7.4493 1
< 0.1%
-7.4166 1
< 0.1%
ValueCountFrequency (%)
1 3
0.1%
0.999 1
 
< 0.1%
0.9935 1
 
< 0.1%
0.9497 1
 
< 0.1%
0.9077 1
 
< 0.1%
0.9029 1
 
< 0.1%
0.9 1
 
< 0.1%
0.874 1
 
< 0.1%
0.8613 1
 
< 0.1%
0.8598 1
 
< 0.1%

NWDCALF
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1272
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22117716
Minimum-126.8879
Maximum7.595
Zeros764
Zeros (%)14.5%
Negative68
Negative (%)1.3%
Memory size41.3 KiB
2023-04-15T11:59:37.983306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-126.8879
5-th percentile0
Q10.32895
median0.3719
Q30.4
95-th percentile0.51654
Maximum7.595
Range134.4829
Interquartile range (IQR)0.07105

Descriptive statistics

Standard deviation2.4453487
Coefficient of variation (CV)11.056064
Kurtosis1859.1496
Mean0.22117716
Median Absolute Deviation (MAD)0.0281
Skewness-40.624434
Sum1164.9401
Variance5.9797305
MonotonicityNot monotonic
2023-04-15T11:59:38.293460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 764
 
14.5%
0.4 400
 
7.6%
0.3957 265
 
5.0%
0.3739 239
 
4.5%
0.3721 236
 
4.5%
0.3487 226
 
4.3%
0.3459 191
 
3.6%
0.3692 190
 
3.6%
0.3575 189
 
3.6%
0.3807 182
 
3.5%
Other values (1262) 2385
45.3%
ValueCountFrequency (%)
-126.8879 1
 
< 0.1%
-92.683 1
 
< 0.1%
-60.6776 1
 
< 0.1%
-25.6047 1
 
< 0.1%
-23.5 1
 
< 0.1%
-15.269 1
 
< 0.1%
-13.9843 1
 
< 0.1%
-13.7861 1
 
< 0.1%
-8.3896 1
 
< 0.1%
-7.9746 6
0.1%
ValueCountFrequency (%)
7.595 1
 
< 0.1%
1 3
0.1%
0.999 1
 
< 0.1%
0.9925 1
 
< 0.1%
0.9497 1
 
< 0.1%
0.9215 1
 
< 0.1%
0.9029 1
 
< 0.1%
0.9 1
 
< 0.1%
0.874 1
 
< 0.1%
0.8613 1
 
< 0.1%

SECALF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct158
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17210387
Minimum0
Maximum0.8165
Zeros1313
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2023-04-15T11:59:38.519542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00155
median0.23
Q30.23
95-th percentile0.23
Maximum0.8165
Range0.8165
Interquartile range (IQR)0.22845

Descriptive statistics

Standard deviation0.10169973
Coefficient of variation (CV)0.59092064
Kurtosis-0.26111644
Mean0.17210387
Median Absolute Deviation (MAD)0
Skewness-0.95155017
Sum906.4711
Variance0.010342835
MonotonicityNot monotonic
2023-04-15T11:59:38.749042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.23 3793
72.0%
0 1313
 
24.9%
0.2142 2
 
< 0.1%
0.1525 2
 
< 0.1%
0.0007 2
 
< 0.1%
0.1614 2
 
< 0.1%
0.2523 2
 
< 0.1%
0.2443 1
 
< 0.1%
0.3045 1
 
< 0.1%
0.1879 1
 
< 0.1%
Other values (148) 148
 
2.8%
ValueCountFrequency (%)
0 1313
24.9%
0.0007 2
 
< 0.1%
0.0009 1
 
< 0.1%
0.001 1
 
< 0.1%
0.0021 1
 
< 0.1%
0.0076 1
 
< 0.1%
0.0079 1
 
< 0.1%
0.0108 1
 
< 0.1%
0.0109 1
 
< 0.1%
0.0125 1
 
< 0.1%
ValueCountFrequency (%)
0.8165 1
< 0.1%
0.6754 1
< 0.1%
0.5896 1
< 0.1%
0.5114 1
< 0.1%
0.5112 1
< 0.1%
0.4756 1
< 0.1%
0.4279 1
< 0.1%
0.4116 1
< 0.1%
0.4064 1
< 0.1%
0.382 1
< 0.1%

WDBMCAIC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1360
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.4870797
Minimum-742.181
Maximum213.301
Zeros3500
Zeros (%)66.5%
Negative1720
Negative (%)32.7%
Memory size41.3 KiB
2023-04-15T11:59:39.013660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-742.181
5-th percentile-29.6855
Q1-0.4
median0
Q30
95-th percentile0
Maximum213.301
Range955.482
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation31.515497
Coefficient of variation (CV)-4.8581948
Kurtosis116.34563
Mean-6.4870797
Median Absolute Deviation (MAD)0
Skewness-8.5946507
Sum-34167.449
Variance993.22656
MonotonicityNot monotonic
2023-04-15T11:59:39.305952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3500
66.5%
-0.8 34
 
0.6%
-0.4 28
 
0.5%
-1.2 16
 
0.3%
-0.009 14
 
0.3%
-2 14
 
0.3%
-8 13
 
0.2%
-4 13
 
0.2%
-2.4 12
 
0.2%
-0.04 10
 
0.2%
Other values (1350) 1613
30.6%
ValueCountFrequency (%)
-742.181 1
< 0.1%
-535.931 1
< 0.1%
-481.323 1
< 0.1%
-417.56 1
< 0.1%
-341.442 1
< 0.1%
-336.78 1
< 0.1%
-325.65 1
< 0.1%
-322.886 1
< 0.1%
-316.444 1
< 0.1%
-297.167 1
< 0.1%
ValueCountFrequency (%)
213.301 1
< 0.1%
183.442 1
< 0.1%
154.488 1
< 0.1%
63.319 1
< 0.1%
45.797 1
< 0.1%
36.574 1
< 0.1%
30.071 1
< 0.1%
23.28 1
< 0.1%
21.766 1
< 0.1%
21.36 1
< 0.1%

NWDBMCAIC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1337
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.7315136
Minimum-653.346
Maximum204.838
Zeros3500
Zeros (%)66.5%
Negative1717
Negative (%)32.6%
Memory size41.3 KiB
2023-04-15T11:59:39.519696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-653.346
5-th percentile-26.2545
Q1-0.3715
median0
Q30
95-th percentile0
Maximum204.838
Range858.184
Interquartile range (IQR)0.3715

Descriptive statistics

Standard deviation27.792968
Coefficient of variation (CV)-4.8491499
Kurtosis117.1748
Mean-5.7315136
Median Absolute Deviation (MAD)0
Skewness-8.5608354
Sum-30187.882
Variance772.4491
MonotonicityNot monotonic
2023-04-15T11:59:39.727208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3500
66.5%
-0.8 34
 
0.6%
-0.4 30
 
0.6%
-1.2 16
 
0.3%
-2 14
 
0.3%
-0.007 13
 
0.2%
-8 13
 
0.2%
-4 13
 
0.2%
-2.4 12
 
0.2%
-4.4 9
 
0.2%
Other values (1327) 1613
30.6%
ValueCountFrequency (%)
-653.346 1
< 0.1%
-472.316 1
< 0.1%
-434.588 1
< 0.1%
-338.39 1
< 0.1%
-325.68 1
< 0.1%
-311.021 1
< 0.1%
-302.695 1
< 0.1%
-283.986 1
< 0.1%
-276.486 1
< 0.1%
-270.078 1
< 0.1%
ValueCountFrequency (%)
204.838 1
< 0.1%
162.616 1
< 0.1%
154.488 1
< 0.1%
64.444 1
< 0.1%
38.066 1
< 0.1%
37.758 1
< 0.1%
27.805 1
< 0.1%
23.787 1
< 0.1%
21.36 1
< 0.1%
18.516 1
< 0.1%

WDBMCAEC
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct887
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7303277
Minimum-6964.167
Maximum513.2
Zeros4101
Zeros (%)77.9%
Negative47
Negative (%)0.9%
Memory size41.3 KiB
2023-04-15T11:59:39.945924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-6964.167
5-th percentile0
Q10
median0
Q30
95-th percentile35.9436
Maximum513.2
Range7477.367
Interquartile range (IQR)0

Descriptive statistics

Standard deviation114.40567
Coefficient of variation (CV)24.185569
Kurtosis2698.0438
Mean4.7303277
Median Absolute Deviation (MAD)0
Skewness-46.607874
Sum24914.636
Variance13088.657
MonotonicityNot monotonic
2023-04-15T11:59:40.244809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4101
77.9%
11.27 46
 
0.9%
11.5 14
 
0.3%
8 12
 
0.2%
10 10
 
0.2%
20 9
 
0.2%
16 8
 
0.2%
9.2 8
 
0.2%
43.2 7
 
0.1%
12 6
 
0.1%
Other values (877) 1046
 
19.9%
ValueCountFrequency (%)
-6964.167 1
< 0.1%
-2666.275 1
< 0.1%
-2190.349 1
< 0.1%
-744.93 1
< 0.1%
-631.244 1
< 0.1%
-533.252 1
< 0.1%
-486.897 1
< 0.1%
-311.501 1
< 0.1%
-266.416 1
< 0.1%
-199.524 1
< 0.1%
ValueCountFrequency (%)
513.2 1
< 0.1%
494.261 1
< 0.1%
480 1
< 0.1%
471.168 1
< 0.1%
391.776 1
< 0.1%
380 1
< 0.1%
368 1
< 0.1%
362 1
< 0.1%
351.904 1
< 0.1%
340 1
< 0.1%

NWDBMCAEC
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct893
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8655215
Minimum-5788.626
Maximum513.2
Zeros4101
Zeros (%)77.9%
Negative49
Negative (%)0.9%
Memory size41.3 KiB
2023-04-15T11:59:40.566859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-5788.626
5-th percentile0
Q10
median0
Q30
95-th percentile33.9854
Maximum513.2
Range6301.826
Interquartile range (IQR)0

Descriptive statistics

Standard deviation99.032756
Coefficient of variation (CV)20.353986
Kurtosis2328.002
Mean4.8655215
Median Absolute Deviation (MAD)0
Skewness-42.314632
Sum25626.702
Variance9807.4868
MonotonicityNot monotonic
2023-04-15T11:59:40.858070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4101
77.9%
11.27 46
 
0.9%
11.5 14
 
0.3%
8 12
 
0.2%
10 10
 
0.2%
20 9
 
0.2%
9.2 8
 
0.2%
16 8
 
0.2%
43.2 7
 
0.1%
128 6
 
0.1%
Other values (883) 1046
 
19.9%
ValueCountFrequency (%)
-5788.626 1
< 0.1%
-2465.368 1
< 0.1%
-1941.683 1
< 0.1%
-758.16 1
< 0.1%
-651.668 1
< 0.1%
-512.094 1
< 0.1%
-486.897 1
< 0.1%
-338.311 1
< 0.1%
-244.304 1
< 0.1%
-162.715 1
< 0.1%
ValueCountFrequency (%)
513.2 1
< 0.1%
480 1
< 0.1%
471.168 1
< 0.1%
391.776 1
< 0.1%
380 1
< 0.1%
368 1
< 0.1%
362 1
< 0.1%
351.904 1
< 0.1%
340 1
< 0.1%
332 1
< 0.1%

Exempt Export Flag
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
False
5114 
True
 
153
ValueCountFrequency (%)
False 5114
97.1%
True 153
 
2.9%
2023-04-15T11:59:41.143068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
True
4067 
False
1200 
ValueCountFrequency (%)
True 4067
77.2%
False 1200
 
22.8%
2023-04-15T11:59:41.336529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

FPN Flag
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
False
3572 
True
1695 
ValueCountFrequency (%)
False 3572
67.8%
True 1695
32.2%
2023-04-15T11:59:41.490928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Interconnector Id
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)1.2%
Missing4591
Missing (%)87.2%
Memory size185.3 KiB
FRANCE
154 
BRITNED
132 
IFA2
124 
NEMOLINK
100 
ELECLINK
84 
Other values (3)
82 

Length

Max length8
Median length7
Mean length6.1715976
Min length3

Characters and Unicode

Total characters4172
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRITNED
2nd rowELECLINK
3rd rowEWIC
4th rowFRANCE
5th rowIFA2

Common Values

ValueCountFrequency (%)
FRANCE 154
 
2.9%
BRITNED 132
 
2.5%
IFA2 124
 
2.4%
NEMOLINK 100
 
1.9%
ELECLINK 84
 
1.6%
EWIC 38
 
0.7%
MOYLE 36
 
0.7%
NSL 8
 
0.2%
(Missing) 4591
87.2%

Length

2023-04-15T11:59:41.645105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-15T11:59:41.861219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
france 154
22.8%
britned 132
19.5%
ifa2 124
18.3%
nemolink 100
14.8%
eleclink 84
12.4%
ewic 38
 
5.6%
moyle 36
 
5.3%
nsl 8
 
1.2%

Most occurring characters

ValueCountFrequency (%)
E 628
15.1%
N 578
13.9%
I 478
11.5%
L 312
7.5%
R 286
 
6.9%
F 278
 
6.7%
A 278
 
6.7%
C 276
 
6.6%
K 184
 
4.4%
M 136
 
3.3%
Other values (8) 738
17.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4048
97.0%
Decimal Number 124
 
3.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 628
15.5%
N 578
14.3%
I 478
11.8%
L 312
7.7%
R 286
7.1%
F 278
6.9%
A 278
6.9%
C 276
6.8%
K 184
 
4.5%
M 136
 
3.4%
Other values (7) 614
15.2%
Decimal Number
ValueCountFrequency (%)
2 124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4048
97.0%
Common 124
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 628
15.5%
N 578
14.3%
I 478
11.8%
L 312
7.7%
R 286
7.1%
F 278
6.9%
A 278
6.9%
C 276
6.8%
K 184
 
4.5%
M 136
 
3.4%
Other values (7) 614
15.2%
Common
ValueCountFrequency (%)
2 124
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 628
15.1%
N 578
13.9%
I 478
11.5%
L 312
7.5%
R 286
 
6.9%
F 278
 
6.7%
A 278
 
6.7%
C 276
 
6.6%
K 184
 
4.4%
M 136
 
3.3%
Other values (8) 738
17.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.3 KiB
Minimum2023-01-03 00:00:00
Maximum2023-07-03 00:00:00
2023-04-15T11:59:42.045712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:42.238813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

Manual Credit Qualifying Flag
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
False
5266 
True
 
1
ValueCountFrequency (%)
False 5266
> 99.9%
True 1
 
< 0.1%
2023-04-15T11:59:42.506920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Credit Qualifying Status
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing39
Missing (%)0.7%
Memory size10.4 KiB
False
4833 
True
 
395
(Missing)
 
39
ValueCountFrequency (%)
False 4833
91.8%
True 395
 
7.5%
(Missing) 39
 
0.7%
2023-04-15T11:59:42.714085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Interactions

2023-04-15T11:59:27.949965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:06.274863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:08.658142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:11.238557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:13.883398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:16.557540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:19.094337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:21.571785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:23.858720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:25.724374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:28.172164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:06.495569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:08.896020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:11.447747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:14.124986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:16.810945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:19.331571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:22.052756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:24.032573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:25.897511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:28.435163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:06.761819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:09.158566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:11.657588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:14.407436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:17.094270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:19.575253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:22.244115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:24.227671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:26.099139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:28.713867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:07.017845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:09.431283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:11.864450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:14.686782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:17.374139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:19.839818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:22.497348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:24.425258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:26.327947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:28.983929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:07.277523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:09.708264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:12.075608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:14.964591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:17.647961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:20.107678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:22.700969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:24.644982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:26.539020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:29.240669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:07.480045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:09.962564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:12.636217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:15.231940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:17.901428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:20.369053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:22.870955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:24.822746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:26.779897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:29.526405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:07.735861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:10.236847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:12.855513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:15.512142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:18.172783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:20.638743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:23.083054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:25.012948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:27.048650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:29.766594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:07.956372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:10.490561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:13.104293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:15.754545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:18.405123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:20.846501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:23.267151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:25.169704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:27.285865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:30.011577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:08.169626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:10.711776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:13.346188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:16.008356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:18.600311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:21.092478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:23.474202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:25.363622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:27.478035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:30.291283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:08.417848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:10.975666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:13.624352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:16.276648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:18.851665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:21.327094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:23.670314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:25.543605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-15T11:59:27.680178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-04-15T11:59:43.399601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
TLFGCDCWDCALFNWDCALFSECALFWDBMCAICNWDBMCAICWDBMCAECNWDBMCAECBMU TypeGSP Group IdGSP Group NameTrading Unit NameProd/Cons FlagProd/Cons StatusExempt Export FlagBase TU FlagFPN FlagInterconnector IdManual Credit Qualifying FlagCredit Qualifying Status
TLF1.000-0.069-0.0310.1100.0460.0050.0140.016-0.130-0.1310.1960.9990.9990.9950.0000.1620.1020.2620.2010.9970.0000.175
GC-0.0691.000-0.061-0.420-0.179-0.459-0.133-0.1350.7100.7080.2850.0000.0000.4710.7340.6880.0360.5060.3960.2190.0000.256
DC-0.031-0.0611.000-0.028-0.1520.3110.7360.734-0.189-0.1870.2800.0400.0400.0000.7530.0980.0290.4400.3730.2050.0000.066
WDCALF0.110-0.420-0.0281.0000.8430.588-0.371-0.370-0.127-0.1270.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.0000.000
NWDCALF0.046-0.179-0.1520.8431.0000.313-0.507-0.5110.1510.1530.0000.0090.0090.0001.0000.0000.0000.0110.0001.0000.0000.000
SECALF0.005-0.4590.3110.5880.3131.0000.0410.046-0.213-0.2150.4430.0180.0180.2190.0000.6840.2740.9310.6911.0000.0000.489
WDBMCAIC0.014-0.1330.736-0.371-0.5070.0411.0000.997-0.361-0.3580.0440.0280.0280.0970.0000.0230.0000.0610.2071.0000.0000.000
NWDBMCAIC0.016-0.1350.734-0.370-0.5110.0460.9971.000-0.362-0.3630.0510.0280.0280.0920.0000.0210.0000.0620.1901.0000.0000.000
WDBMCAEC-0.1300.710-0.189-0.1270.151-0.213-0.361-0.3621.0000.9970.0000.0000.0000.0001.0000.0000.0000.0000.0071.0000.0000.000
NWDBMCAEC-0.1310.708-0.187-0.1270.153-0.215-0.358-0.3630.9971.0000.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.0000.000
BMU Type0.1960.2850.2800.0000.0000.4430.0440.0510.0000.0001.0000.1420.1420.6050.0000.7000.7510.9820.7081.0000.0180.787
GSP Group Id0.9990.0000.0400.0000.0090.0180.0280.0280.0000.0000.1421.0001.0000.9990.3040.0870.0950.0680.0670.0001.0000.098
GSP Group Name0.9990.0000.0400.0000.0090.0180.0280.0280.0000.0000.1421.0001.0000.9990.3040.0870.0950.0680.0670.0001.0000.098
Trading Unit Name0.9950.4710.0000.0000.0000.2190.0970.0920.0000.0000.6050.9990.9991.0000.3450.8190.1580.9940.3400.0001.0000.687
Prod/Cons Flag0.0000.7340.7531.0001.0000.0000.0000.0001.0001.0000.0000.3040.3040.3451.0000.9980.0000.0000.0590.0000.0000.000
Prod/Cons Status0.1620.6880.0980.0000.0000.6840.0230.0210.0000.0000.7000.0870.0870.8190.9981.0000.1900.6610.5300.0000.0000.582
Exempt Export Flag0.1020.0360.0290.0000.0000.2740.0000.0000.0000.0000.7510.0950.0950.1580.0000.1901.0000.0000.1741.0000.0000.472
Base TU Flag0.2620.5060.4400.0000.0110.9310.0610.0620.0000.0000.9820.0680.0680.9940.0000.6610.0001.0000.6681.0000.0000.376
FPN Flag0.2010.3960.3730.0000.0000.6910.2070.1900.0070.0000.7080.0670.0670.3400.0590.5300.1740.6681.0001.0000.0000.418
Interconnector Id0.9970.2190.2051.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0001.0001.0001.0001.0000.0001.000
Manual Credit Qualifying Flag0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0181.0001.0001.0000.0000.0000.0000.0000.0000.0001.0000.000
Credit Qualifying Status0.1750.2560.0660.0000.0000.4890.0000.0000.0000.0000.7870.0980.0980.6870.0000.5820.4720.3760.4181.0000.0001.000

Missing values

2023-04-15T11:59:30.754326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-15T11:59:31.597961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-15T11:59:32.525896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

BM Unit IDBMU NameParty NameParty IDBMU TypeNGC BMU NameGSP Group IdGSP Group NameTrading Unit NameProd/Cons FlagProd/Cons StatusTLFGCDCWDCALFNWDCALFSECALFWDBMCAICNWDBMCAICWDBMCAECNWDBMCAECExempt Export FlagBase TU FlagFPN FlagInterconnector IdEffective FromManual Credit Qualifying FlagCredit Qualifying Status
02__AALAB000ALAB_ASquare1 Energy LimitedALABGNaN_AEastern GSP GroupDEFAULT__ANaNC0.0027630.0000.000.39550.34870.230.0000.0000.0000.000FTFNaN2023-01-03FF
12__AALAS000ALAS_AClean Energy Supply LtdALASGNaN_AEastern GSP GroupDEFAULT__ANaNC0.0027630.0000.000.39550.34870.230.0000.0000.0000.000FTFNaN2023-01-03FF
22__AALTI0002__AALTI000Co-operative Energy LimitedVOLAGNaN_AEastern GSP GroupDEFAULT__ANaNC0.0027630.000-4.380.39550.34870.23-1.732-1.5270.0000.000FTFNaN2023-01-03FF
32__AANGE0002__AANGE000Limejump Energy LimitedANGELGNaN_AEastern GSP GroupDEFAULT__ANaNC0.002763139.329-20.520.11660.11800.23-2.393-2.42116.24616.441FTFNaN2023-01-03FF
42__AANGE0012__AANGE001Limejump Energy LimitedANGELSNaN_AEastern GSP GroupDEFAULT__ANaNC0.00276380.000-1.000.39550.34870.23-0.396-0.34931.64027.896FTFNaN2023-01-03FF
52__AANGE0022__AANGE002Limejump Energy LimitedANGELSAG-ALIM03_AEastern GSP GroupDEFAULT__ANaNC0.00276350.0000.000.39550.34870.230.0000.00011.50011.500FTTNaN2023-01-03FF
62__AANGE0032__AANGE003Limejump Energy LimitedANGELSNaN_AEastern GSP GroupDEFAULT__ANaNC0.0027630.0000.000.39550.34870.230.0000.0000.0000.000FTFNaN2023-01-03FF
72__AANGE0042__AANGE004Limejump Energy LimitedANGELSNaN_AEastern GSP GroupDEFAULT__ANaNC0.0027630.0000.000.39550.34870.230.0000.0000.0000.000FTFNaN2023-01-03FF
82__AARIZ000ARIZ_ABritish Gas Trading LtdBRITGASGNaN_AEastern GSP GroupDEFAULT__ANaNC0.0027630.0000.000.39550.34870.230.0000.0000.0000.000FTFNaN2023-01-03FF
92__AARRU0002__AARRU000British Gas Trading LtdBRITGASGNaN_AEastern GSP GroupDEFAULT__ANaNC0.0027630.000-1.340.39550.34870.23-0.530-0.4670.0000.000FTFNaN2023-01-03FF
BM Unit IDBMU NameParty NameParty IDBMU TypeNGC BMU NameGSP Group IdGSP Group NameTrading Unit NameProd/Cons FlagProd/Cons StatusTLFGCDCWDCALFNWDCALFSECALFWDBMCAICNWDBMCAICWDBMCAECNWDBMCAECExempt Export FlagBase TU FlagFPN FlagInterconnector IdEffective FromManual Credit Qualifying FlagCredit Qualifying Status
5257V__LCEND005V__LCEND005Centrica Business Solutions UKCENDEPUKVAG-CBS05L_LSouth WesternNaNPP0.0125250.00.00.00.00.00.00.00.00.0FFTNaN2023-01-03FNaN
5258V__LFLEX001V__LFLEX001Flexitricity LimitedFLEXTRCYVAG-FLX00L_LSouth WesternNaNCC0.0125250.00.00.00.00.00.00.00.00.0FFTNaN2023-01-03FNaN
5259V__MADEL001ADELA_SBMU_MAdela Energy LtdADELAVAG-ADL01M_MYorkshire ElectricityNaNPP-0.0083700.00.00.00.00.00.00.00.00.0FFTNaN2023-01-03FNaN
5260V__MCEND002Allen DieselsCentrica Business Solutions UKCENDEPUKVCBSDIS03M_MYorkshire ElectricityNaNPP-0.0083700.00.00.00.00.00.00.00.00.0FFTNaN2023-01-03FNaN
5261V__MEROV001V__MEROV001Erova Energy LimitedEROVAIRLVAG-ERV01M_MYorkshire ElectricityNaNCC-0.0083700.00.00.00.00.00.00.00.00.0FFTNaN2023-01-03FNaN
5262V__MFLEX001V__MFLEX001Flexitricity LimitedFLEXTRCYVAG-FLX06M_MYorkshire ElectricityNaNCC-0.0083700.00.00.00.00.00.00.00.00.0FFTNaN2023-01-03FNaN
5263V__MGBLO001V__MGBLO001GridBeyond LimitedENDECOVAG-GBL04M_MYorkshire ElectricityNaNNaNC-0.0083700.00.00.00.00.00.00.00.00.0FFFNaN2023-01-03FNaN
5264V__NFLEX001V__NFLEX001Flexitricity LimitedFLEXTRCYVAG-FLX02N_NSOUTH of SCOTLAND GSPNaNCC-0.0042620.00.00.00.00.00.00.00.00.0FFTNaN2023-01-03FNaN
5265V__NFLEX002V__NFLEX002Flexitricity LimitedFLEXTRCYVAG-FLX07N_NSOUTH of SCOTLAND GSPNaNCC-0.0042620.00.00.00.00.00.00.00.00.0FFTNaN2023-01-03FNaN
5266V__PFLEX001V__PFLEX001Flexitricity LimitedFLEXTRCYVAG-FLX01P_PNORTH of SCOTLAND GSPNaNCC-0.0261240.00.00.00.00.00.00.00.00.0FFTNaN2023-01-03FNaN